Over the past few decades, medical imaging techniques have been extensively investigated and various applications have been developed. One such application is the measurement of blood flow (e.g., cerebral blood flow (CBF)). CBF images can be relative or quantitative. In a relative CBF image, regions of high flow are distinguished from regions of low flow, but there is no quantitative scale for the imaging results. A quantitative CBF image provides this scale, and usefully provides more complete information. For example, quantitative CBF can help determine whether or not regions of relatively low blood flow have a blood flow that falls below a critical level (e.g., as can occur in a stroke patient).
However, it has proven to be difficult to obtain quantitative CBF images, especially in cases where a non-invasive imaging modality is employed, such as magnetic resonance imaging (MRI). For example, absolute quantitation of dynamic susceptibility contrast (DSC) MRI is challenging due to many uncertainties, including partial volume errors and the nonlinear contrast relaxivity. Arterial spin labeling (ASL) MRI can measure quantitative CBF in regions with rapidly-arriving flow, but CBF is underestimated in regions with delayed arrival.
Several methods have been considered to improve quantitative MRI CBF measurements, with varying levels of success. Lin et al. described a method that derived a correction factor normalizing the area-under-the-curve of the individual patient's superior sagittal sinus (SSS) with the mean value derived from a small cohort of young normal volunteers (J. MRI 14:659-667, 2001). Mukherjee et al. described scaling the individual measurements such that a deep white matter region of interest (ROI) was fixed to be 22 ml/100 g/min, which is a value derived from PET studies. However, Mukherjee et al. reported that this scaling approach did not significantly improve the correlation of DSC and gold-standard PET CBF measurements (Am. J. Neuroradiol. 24:862-871, May 2003). Sakaie et al. demonstrated a method in which steady-state cerebral blood volume (CBV) measurements obtained using T1-weighted images before and after bolus contrast passage could be used to determine a patient-specific correction factor, with the assumption that there is slow-exchange between intra- and extravascular water on the time scale of the measurement (J. MRI 21:512-519, 2005).
In view of the mixed results and added complications of existing blood flow image correction approaches, it would be an advance in the art to provide improved quantitative blood flow image correction.